ECONOMETRICS
- Academic year
- 2020/2021 Syllabus of previous years
- Official course title
- ECONOMETRICS
- Course code
- EM2008 (AF:331203 AR:178632)
- Modality
- On campus classes
- ECTS credits
- 6
- Degree level
- Master's Degree Programme (DM270)
- Educational sector code
- SECS-P/05
- Period
- 3rd Term
- Course year
- 1
- Where
- VENEZIA
- Moodle
- Go to Moodle page
Contribution of the course to the overall degree programme goals
Expected learning outcomes
- understand how to specify an econometric model starting from an economic model
- knowledge of the assumptions underlying each econometric model and command of the analytical tools needed for quantitative analyses
- understand the economic and financial phenomena related in particular to financial markets, by means of the most recent models of financial economics and econometrics
Application of acquired knowledge and skills:
- interpretation and management of financial dynamics, through the use of advanced analytical tools covered in the lectures
- being able to design useful strategies to measure and quantify economic phenomena and relationships among financial and macroeconomic variables
- know how to solve problems of particular interest in the econometrics for finance by exploiting analytical tools and empirical analyses
Judgement and interpretation skills:
- evaluate strengths and weaknesses of the methodologies analysed and of their empirical application
- being able to critically interpret the outcomes of empirical analyses
Pre-requirements
Matrix Algebra
Differential Calculus
Integral Calculus
Statistical Tools:
Random Variables and Distribution Theory
Point and Interval Estimation
Hypothesis Testing
Least Squares and Standard Linear Model
Contents
1. The Multiple Linear Regression Model
- Matrix formulation of the k-Variable Model; The algebra of least squares; Partial correlation coefficients; Geometry of least squares; Inference in the k-variable equation; Prediction
2. Some Tests of the k-Variable Linear Equation for Specification Error
- Specification error; Model evaluation and diagnostic tests; Tests of parameter constancy; Tests of structural change; Dummy variables
3. Maximum Likelihood (ML), Generalized Least Squares (GLS), and Instrumental Variable (IV) Estimators
- Maximum Likelihood estimators; ML estimation of the linear model; Likelihood ratio, Wald and Lagrange Multiplier Tests; Generalized Least Squares; Instrumental Variable estimators
4. Heteroscedasticity and Autocorrelation
- Properties of OLS estimators; Tests for heteroskedasticity and autocorrelation; Estimation under heteroskedasticity and with autocorrelated disturbances
PART 2: REGRESSION ANALYSIS WITH TIME SERIES DATA
5. Stationary univariate time series
- Univariate stochastic processes; ARMA models; autocorrelation and autocovariance functions; Wold's decomposition and invertible processes; Box-Jenkins selection
6. Modeling volatility
- ARCH and GARCH processes
7. Non-stationary univariate stochastic processes
- Models with trend; deterministic and stochastic trends; trend stationary and difference stationary series; integrated processes; unit root and stationarity tests
References:
8. Multivariate time series models with stationary regressors
- Autoregressive Distributed Lag (ADL) model; impact and long-run multipliers; impulse response function; Error correction model; Partial adjustment model
9. Multivariate time series models with integrated variables
- Linear combinations of integrated variables; spurios regressions; cointegration and ECM, testing for cointegration: Engle and Granger methodology
10. Multiple Equation Models
- Vector Autoregressions (VARs); Estimation of VARs; Vector Error Correction Models; Cointegration in VAR models; Johansen Methodology
PART 3: ADVANCED TOPICS
11. Panel data models
- Fixed and random effects models, correlated random effects, dynamic panel data models
12. Limited dependent variable models
- Linear probability model, Logit and Probit models, MLEs for binary choice model
Referral texts
- Johnston, J. and J. Dinardo (1997), Econometric Methods, 4th edition, McGraw-Hill, New York.
- Ghysels, E. and M. Marcellino (2018), Applied economic forecasting using time series methods, Oxford University Press.
- Enders, W. (2015), Applied Econometric Time Series, 4th edition, Wiley.
- Verbeek, M. (2017), A guide to modern econometrics, 5th Edition, Wiley,-
Additional references:
- Lectures slides made available on Moodle during the course
- Vogelvang B. (2005), Econometrics - Theory and Applications with EViews, FT Prentice Hall.
- Marcellino M. (2016), Applied Econometrics: An Introduction, EGEA.
Assessment methods
Teaching methods
Further information
Accommodation and support services for students with disabilities and students with specific learning impairments
Ca' Foscari abides by Italian Law (Law 17/1999; Law 170/2010) regarding support services and accommodation available to students with disabilities. This includes students with mobility, visual, hearing and other disabilities (Law 17/1999), and specific learning impairments (Law 170/2010). If you have a disability or impairment that requires accommodations (i.e., alternate testing, readers, note takers or interpreters) please contact the Disability and Accessibility Offices in Student Services: disabilita@unive.it.
Type of exam
2030 Agenda for Sustainable Development Goals
This subject deals with topics related to the macro-area "Circular economy, innovation, work" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development